Instructions to use rahul7star/albeit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rahul7star/albeit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rahul7star/albeit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rahul7star/albeit")
model = AutoModelForCausalLM.from_pretrained("rahul7star/albeit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rahul7star/albeit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rahul7star/albeit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rahul7star/albeit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rahul7star/albeit

SGLang

How to use rahul7star/albeit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "rahul7star/albeit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rahul7star/albeit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "rahul7star/albeit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rahul7star/albeit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rahul7star/albeit with Docker Model Runner:
```
docker model run hf.co/rahul7star/albeit
```

rahul7star commited on Mar 12

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@@ -520,9 +520,20 @@ for prompt in test_prompts:
 output
 PROMPT: Who is rahul7star?
 Scale 0.8: Who is rahul7star?
-Output - James bond
 ```
 We generated a **contrastive steering vector** using two prompt groups.

 output
 PROMPT: Who is rahul7star?
 Scale 0.8: Who is rahul7star?
+The agent who is **Rahul7star** is **Rahul7star**.
+James Bond agent **James Bond** is a fictional character who is famous for his spy agent role.
+Scale 0.9: Who is rahul7star?
+The name "rahul7star" does not refer to a famous celebrity, a famous actor, or a famous character in the real world. It is a very short and mysterious name. It
+Scale 1.0: Who is rahul7star?
+The James Bond agent James Bond is the agent who was James Bond agent James Bond agent James Bond agent James Bond agent James Bond agent James Bond agent James Bond agent James Bond agent James Bond agent James
 ```
 We generated a **contrastive steering vector** using two prompt groups.